scholarly journals CLASS-PAIR-GUIDED MULTIPLE KERNEL LEARNING OF INTEGRATING HETEROGENEOUS FEATURES FOR CLASSIFICATION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 195-200
Author(s):  
Q. Wang ◽  
Y. Gu ◽  
T. Liu ◽  
H. Liu ◽  
X. Jin
Keyword(s):  
Multiple Kernel Learning ◽  
Real Data ◽  
Classification Problem ◽  
Kernel Learning ◽  
Data Sets ◽  
Specific Class ◽  
Multiple Kernel ◽  
Remote Sensing Image Classification ◽  
Heterogeneous Features ◽  
Class Pair

In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs) from multispectral image (MSI) and light detection and ranging (LiDAR) data. In particular, the <q>one-against-one</q> strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA) in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

scholarly journals Multiple kernel learning for integrative consensus clustering of omic datasets

2020 ◽  
Vol 36 (18) ◽  
pp. 4789-4796
Author(s):  
Alessandra Cabassi ◽  
Paul D W Kirk
Keyword(s):  
Multiple Kernel Learning ◽  
Real Data ◽  
Kernel Learning ◽  
Supplementary Information ◽  
Consensus Clustering ◽  
Multiple Kernel ◽  
Multiple Data ◽  
Module Discovery ◽  
Cluster Of Clusters ◽  
R Packages

Abstract Motivation Diverse applications—particularly in tumour subtyping—have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear. Results We rigorously benchmark COCA, and present Kernel Learning Integrative Clustering (KLIC) as an alternative strategy. KLIC frames the challenge of combining clustering structures as a multiple kernel learning problem, in which different datasets each provide a weighted contribution to the final clustering. This allows the contribution of noisy datasets to be down-weighted relative to more informative datasets. We compare the performances of KLIC and COCA in a variety of situations through simulation studies. We also present the output of KLIC and COCA in real data applications to cancer subtyping and transcriptional module discovery. Availability and implementation R packages klic and coca are available on the Comprehensive R Archive Network. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modelling and Recognition of Protein Contact Networks by Multiple Kernel Learning and Dissimilarity Representations

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 794
Author(s):  
Alessio Martino ◽  
Enrico De Santis ◽  
Alessandro Giuliani ◽  
Antonello Rizzi
Keyword(s):  
Knowledge Discovery ◽  
Classification System ◽  
Multiple Kernel Learning ◽  
Classification Problem ◽  
Kernel Functions ◽  
Kernel Learning ◽  
Biological Knowledge ◽  
Training Procedure ◽  
Kernel Weights ◽  
Multiple Kernel

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins’ functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system.

scholarly journals Self-Adaptive Deep Multiple Kernel Learning Based on Rademacher Complexity

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 325 ◽  
Author(s):  
Shengbing Ren ◽  
Wangbo Shen ◽  
Chaudry Siddique ◽  
You Li
Keyword(s):  
Kernel Function ◽  
Multiple Kernel Learning ◽  
Fixed Number ◽  
Kernel Learning ◽  
Model Parameters ◽  
Test Accuracy ◽  
Data Sets ◽  
Suitable Model ◽  
Multiple Kernel ◽  
Self Adaptive

The deep multiple kernel learning (DMKL) method has caused widespread concern due to its better results compared with shallow multiple kernel learning. However, existing DMKL methods, which have a fixed number of layers and fixed type of kernels, have poor ability to adapt to different data sets and are difficult to find suitable model parameters to improve the test accuracy. In this paper, we propose a self-adaptive deep multiple kernel learning (SA-DMKL) method. Our SA-DMKL method can adapt the model through optimizing the model parameters of each kernel function with a grid search method and change the numbers and types of kernel function in each layer according to the generalization bound that is evaluated with Rademacher chaos complexity. Experiments on the three datasets of University of California—Irvine (UCI) and image dataset Caltech 256 validate the effectiveness of the proposed method on three aspects.

scholarly journals A sequence-based multiple kernel model for identifying DNA-binding proteins

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuqing Qian ◽  
Limin Jiang ◽  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Multiple Kernel Learning ◽  
Dna Binding Proteins ◽  
Kernel Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
System A ◽  
Multiple Kernel

Abstract Background DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ($$84.19\%$$ 84.19 % ) and PDB186 ($$83.7\%$$ 83.7 % ), respectively. Conclusion Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

Multiple Kernel Learning for Remote Sensing Image Classification

2018 ◽  
Vol 56 (3) ◽  
pp. 1425-1443 ◽  
Author(s):  
Saeid Niazmardi ◽  
Begum Demir ◽  
Lorenzo Bruzzone ◽  
Abdolreza Safari ◽  
Saeid Homayouni
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Multiple Kernel Learning ◽  
Remote Sensing Image ◽  
Kernel Learning ◽  
Multiple Kernel ◽  
Remote Sensing Image Classification
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